update model card README.md

README.md

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+---
+license: mit
+tags:
+- generated_from_trainer
+datasets:
+- lg-ner
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+model-index:
+- name: luganda-ner-v2
+  results:
+  - task:
+      name: Token Classification
+      type: token-classification
+    dataset:
+      name: lg-ner
+      type: lg-ner
+      config: lug
+      split: test
+      args: lug
+    metrics:
+    - name: Precision
+      type: precision
+      value: 0.9352766798418972
+    - name: Recall
+      type: recall
+      value: 0.9288518155053974
+    - name: F1
+      type: f1
+      value: 0.93205317577548
+    - name: Accuracy
+      type: accuracy
+      value: 0.9817219554779573
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# luganda-ner-v2
+
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the lg-ner dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0955
+- Precision: 0.9353
+- Recall: 0.9289
+- F1: 0.9321
+- Accuracy: 0.9817
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
+| 0.5913        | 1.0   | 609  | 0.2667          | 0.6740    | 0.7620 | 0.7153 | 0.9336   |
+| 0.2461        | 2.0   | 1218 | 0.1704          | 0.7981    | 0.8437 | 0.8203 | 0.9562   |
+| 0.1784        | 3.0   | 1827 | 0.1273          | 0.8578    | 0.8943 | 0.8757 | 0.9669   |
+| 0.1337        | 4.0   | 2436 | 0.1048          | 0.8731    | 0.9132 | 0.8927 | 0.9726   |
+| 0.0868        | 5.0   | 3045 | 0.0988          | 0.9129    | 0.9178 | 0.9153 | 0.9760   |
+| 0.0736        | 6.0   | 3654 | 0.0961          | 0.9146    | 0.9225 | 0.9185 | 0.9781   |
+| 0.0602        | 7.0   | 4263 | 0.0877          | 0.9270    | 0.9222 | 0.9246 | 0.9798   |
+| 0.0566        | 8.0   | 4872 | 0.0948          | 0.9281    | 0.9222 | 0.9252 | 0.9807   |
+| 0.0514        | 9.0   | 5481 | 0.0930          | 0.9349    | 0.9271 | 0.9310 | 0.9817   |
+| 0.0395        | 10.0  | 6090 | 0.0955          | 0.9353    | 0.9289 | 0.9321 | 0.9817   |
+
+
+### Framework versions
+
+- Transformers 4.26.1
+- Pytorch 1.13.1+cu116
+- Datasets 2.10.1
+- Tokenizers 0.13.2